Denbeigh, Janet

AbstractBackgroundAdaptive radiotherapy (ART) workflows have been increasingly adopted to achieve dose escalation and tissue sparing under shifting anatomic conditions, but the necessity of recontouring and the associated time burden hinders a real‐time or online ART workflow. In response to this challenge, approaches to auto‐segmentation involving deformable image registration, atlas‐based segmentation, and deep learning‐based segmentation (DLS) have been developed. Despite the particular promise shown by DLS methods, implementing these approaches in a clinical setting remains a challenge, namely due to the difficulty of curating a data set of sufficient size and quality so as to achieve generalizability in a trained model.PurposeTo address this challenge, we have developed an intentional deep overfit learning (IDOL) framework tailored to the auto‐segmentation task. However, certain limitations were identified, particularly the insufficiency of the personalized dataset to effectively overfit the model. In this study, we introduce a personalized hyperspace learning (PHL)‐IDOL segmentation framework capable of generating datasets that induce the model to overfit specific patient characteristics for medical image segmentation.MethodsThe PHL‐IDOL model is trained in two stages. In the first, a conventional, general model is trained with a diverse set of patient data (n = 100 patients) consisting of CT images and clinical contours. Following this, the general model is tuned with a data set consisting of two components: (a) selection of a subset of the patient data (m < n) using the similarity metrics (mean square error (MSE), peak signal‐to‐noise ratio (PSNR), structural similarity index (SSIM), and the universal quality image index (UQI) values); (b) adjust the CT and the clinical contours using a deformed vector generated from the reference patient and the selected patients using (a). After training, the general model, the continual model, the conventional IDOL model, and the proposed PHL‐IDOL model were evaluated using the volumetric dice similarity coefficient (VDSC) and the Hausdorff distance 95% (HD95%) computed for 18 structures in 20 test patients.ResultsImplementing the PHL‐IDOL framework resulted in improved segmentation performance for each patient. The Dice scores increased from 0.810.05 with the general model, 0.83 for the continual model, 0.83 for the conventional IDOL model to an average of 0.87 with the PHL‐IDOL model. Similarly, the Hausdorff distance decreased from 3.06 with the general model, 2.84 for the continual model, 2.79 for the conventional IDOL model and 2.36 for the PHL‐IDOL model. All the standard deviations were decreased by nearly half of the values comparing the general model and the PHL‐IDOL model.ConclusionThe PHL‐IDOL framework applied to the auto‐segmentation task achieves improved performance compared to the general DLS approach, demonstrating the promise of leveraging patient‐specific prior information in a task central to online ART workflows.

AbstractBackgroundKilo‐voltage cone‐beam computed tomography (CBCT) is a prevalent modality used for adaptive radiotherapy (ART) due to its compatibility with linear accelerators and ability to provide online imaging. However, the widely‐used Feldkamp‐Davis‐Kress (FDK) reconstruction algorithm has several limitations, including potential streak aliasing artifacts and elevated noise levels. Iterative reconstruction (IR) techniques, such as total variation (TV) minimization, dictionary‐based methods, and prior information‐based methods, have emerged as viable solutions to address these limitations and improve the quality and applicability of CBCT in ART.PurposeOne of the primary challenges in IR‐based techniques is finding the right balance between minimizing image noise and preserving image resolution. To overcome this challenge, we have developed a new reconstruction technique called high‐resolution CBCT (HRCBCT) that specifically focuses on improving image resolution while reducing noise levels.MethodsThe HRCBCT reconstruction technique builds upon the conventional IR approach, incorporating three components: the data fidelity term, the resolution preservation term, and the regularization term. The data fidelity term ensures alignment between reconstructed values and measured projection data, while the resolution preservation term exploits the high resolution of the initial Feldkamp‐Davis‐Kress (FDK) algorithm. The regularization term mitigates noise during the IR process. To enhance convergence and resolution at each iterative stage, we applied Iterative Filtered Backprojection (IFBP) to the data fidelity minimization process.ResultsWe evaluated the performance of the proposed HRCBCT algorithm using data from two physical phantoms and one head and neck patient. The HRCBCT algorithm outperformed all four different algorithms; FDK, Iterative Filtered Back Projection (IFBP), Compressed Sensing based Iterative Reconstruction (CSIR), and Prior Image Constrained Compressed Sensing (PICCS) methods in terms of resolution and noise reduction for all data sets. Line profiles across three line pairs of resolution revealed that the HRCBCT algorithm delivered the highest distinguishable line pairs compared to the other algorithms. Similarly, the Modulation Transfer Function (MTF) measurements, obtained from the tungsten wire insert on the CatPhan 600 physical phantom, showed a significant improvement with HRCBCT over traditional algorithms.ConclusionThe proposed HRCBCT algorithm offers a promising solution for enhancing CBCT image quality in adaptive radiotherapy settings. By addressing the challenges inherent in traditional IR methods, the algorithm delivers high‐definition CBCT images with improved resolution and reduced noise throughout each iterative step. Implementing the HR CBCT algorithm could significantly impact the accuracy of treatment planning during online adaptive therapy.

High concentrations of bone morphogenetic protein 2 (BMP2) in bone regeneration cause adverse events (e.g, heterotopic bone formation and acute inflammation). This study examines novel epigenetic strategies (i.e., EZH2 inhibition) for augmenting osteogenesis, thereby aiming to reduce the required BMP2 dose in vivo for bone regeneration and minimize these adverse effects. Human bone marrow-derived mesenchymal stem cells (BMSCs) were grown on three-dimensional (3D)-printed medical-grade polycaprolactone scaffolds and incubated in osteogenic media containing 50 ng/mL BMP2 and/or 5 μM GSK126 (EZH2 inhibitor) for 6 days ( n = 3 per group and timepoint). Constructs were harvested for realtime quantitative polymerase chain reaction analysis at Day 10 and immunofluorescence (IF) microscopy at Day 21. After pretreating for 6 days and maintaining in osteogenic media for 4 days, BMSC-seeded scaffolds were also implanted in an immunocompromised subcutaneous murine model ( n = 39; 3/group/donor and 3 control scaffolds) for histological analysis at 8 weeks. Pretreatment of BMSCs with BMP2 and BMP2/GSK126 costimulated expression of osteoblast-related genes (e.g., IBSP , SP7 , RUNX2 , and DLX5 ), as well as protein accumulation (e.g., collagen type 1/ COL1A1 and osteocalcin/ BGLAP ) based on IF staining. While in vivo implantation for 8 weeks did not result in bone formation, increased angiogenesis was observed in BMP2 and BMP2/GSK126 groups. This study finds that BMP2 and GSK126 costimulate osteogenic differentiation of MSCs on 3D scaffolds in vitro and may contribute to enhanced vascularization when implanted in vivo to support bone formation. Thus, epigenetic priming with EZH2 inhibitors may have translational potential in bone healing by permitting a reduction of BMP2 dosing in vivo to mitigate its side effects. Impact statement While autografts are still the gold standard for bone reconstruction, tissue availability and donor morbidity are significant limitations. Previous attempts to use high concentrations of bone morphogenetic protein 2 (BMP2) have been shown to cause adverse events such as excessive bone formation and acute inflammation. Overall, the utilization of EZH2 inhibitors to modulate gene expression in favor of bone healing has been demonstrated in vitro in a tissue engineering strategy. Our study will pave the way to developing tissue engineering strategies involving GSK126 as an adjuvant to increase the effects of BMP2 for stimulating cells of interest on a three-dimensional scaffold for bone regeneration.

Background Twelve paired fresh frozen cadaveric wrists were randomized to a 360-degree tenodesis repair group or the 360-degree tenodesis repair with an internal brace (suture tape) construct. Case Description The specimens were preloaded to 5 N and subsequently biomechanically loaded to failure, at a rate of 0.1 mm/s on a jig that allowed for axial load. The maximum load and mode of failure were recorded. Load to failure in the 360 tenodesis group with internal brace was 283.47 ± 100.25 N, compared with the 360 tenodesis group only, whose yield strength was 143.61 ± 90.54 N. The mode of failure within the internal brace construct was either through knot slippage, graft disruption, or bone separation from strength testing construct. The 360 tenodesis group tended to fail via graft slippage or graft rupture. Literature Review The management of scapholunate instability can be a difficult problem to treat. Traditionally, many of the surgical reconstructions have focused upon dorsal ligament reconstruction with Kirschner (K) wire fixation. This results in prolonged immobilization of the wrist with varied outcomes, in part due to the multiaxial instability that may persist due to concomitant volar ligament disruption. To address this instability, surgical techniques have been devised that address both the volar and dorsal ligament injuries. Clinical Relevance Scapholunate reconstruction with a 360-degree tenodesis and internal brace augmentation (SLITT procedure) provided superior biomechanical stability than tenodesis alone.